FEATURE SELECTION WITH FORWARD SELECTION IN THE K-NEAREST NEIGHBOR METHOD FOR STUDENT COUNSELING PREDICTION
Abstract
One of the most difficult issues to predict in schools is that of guidance and counseling. For individuals who have the authority to make decisions, accurate predictions are needed to support decision-making. KNN is one of the methods that can make predictions, especially for counseling guidance predictions. The KNN algorithm approach used to conduct tests and experiments on 388 student data obtained an accuracy of 96.40%. After optimization using feature selection with forward selection, the accuracy increased by 0.51%. The final result of the accuracy obtained after experiencing an increase in the level of accuracy value of 96.91% with the number of students who do not have to do counseling guidance is as many as 362 students, and 62 students must do counseling guidance.
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